Assessing the Impact of Health Savings Accounts on Insurance and Coverage Costs

Transcription

1 Assessing the Impact of Health Savings Accounts on Insurance and Coverage Costs Stephen T Parente, Ph.D. Roger Feldman, Ph.D. Jean Abraham, Ph.D. Jon B Christianson, Ph.D. University of Minnesota Abstract This analysis discovers new insights offered by the introduction of Consumer Directed Health Plans (CDHPs) into how price affects health insurance choices. Specifically, we find a positive impact of the health care spending account from which medical expenses are debited and negative responses to the new CDHP deductible developed as the difference between a traditional high deductible health plan and the consumer s spending account. With respect to their respective elasticities, the CDHP deductible, known as the donut hole, has a far more elastic price response than the health spending account. In terms of new policy options to reduce the uninsured through tax credits targeted at Health Savings Accounts (HSAs), we find, on average, elastic crossprice responses to increase the takeup of insurance among those without coverage. However, takeup is considerably greater among the higherincome population due to wealthier individuals having a distinct preference for CDHPs among all other health plan choices. JEL Codes: I1, D12, C93 For Presentation at the 2006 ASSA Meeting in Boston, MA December, 2005 Original NBER 2005 Summer Institute Paper Attached as Appendix 1 Do not quote or cite without permission of authors. Research originally presented at the Health Care Program of the 2005 National Bureau of Economic Research Summer Institute, Cambridge, MA, USA on July 29, Corresponding Author: Stephen T. Parente, Ph.D., Assistant Professor, Department of Finance, Carlson School of Management, University of Minnesota, th Ave., Rm. 3149, Minneapolis, MN 55455, , sparente@csom.umn.edu, This paper was funded jointly by the Robert Wood Johnson Foundation's initiative on Changes in Health Care Financing and Organization and DHHS Contract HHSP P: Analytic Support in Assessing the Impact of Health Savings Accounts on Insurance Coverage and Costs.

2 1.0 Introduction Consumer directed health plans (CDHPs) are attracting attention from consumers, employers, and policymakers. CDHPs are highdeductible health insurance plans coupled with a taxadvantaged account that can be used to pay for eligible medical expenses. If an enrollee spends all of the dollars in the health spending account during a given year, she then spends her own money until the health insurance plan deductible requirement is met. Starting from a handful of venture capital funded initiatives in 2001, this market has evolved considerably with CDHP enrollment currently estimated at approximately three to five million covered lives (Gabel et, 2005). The 2003 Medicare Modernization Act (MMA) gave a huge boost to CDHPs by approving taxadvantaged accounts for certain highdeductible health insurance plans. Section 1201 (and subsequent guidance by the Treasury Department) approved a new form of health plan known as a Health Savings Account or HSA. Beginning on January 1, 2004, most nonelderly individuals can purchase a health plan with an annual deductible of at least $1,000 for an individual and $2,000 for a family, coupled with a taxadvantaged account to which both the employer and the enrollee may contribute. Total annual contributions can be as large as the plan s deductible amount (up to $5,000 for an individual and $10,000 for a family). Contributions to the HSA, as well as interest and investment earnings in the HSA, are not taxable, and unlike previous designs, the HSA is fully portable so an individual may use it without being dependent on the provisions of a particular employer. In response to MMA, mainstream insurers such as Blue Cross and Blue Shield plans and UnitedHealth Group have hurried to offer HSAs. 1

3 The purpose of this paper is to examine the effect of price on health plan choice. More specifically, we will determine whether the health care spending account in the new CDHP designs provides an incentive for consumers to choose these plans over conventional health insurance plans; and we will estimate the extent to which the donut hole (i.e. the gap between the account and point where insurance coverage begins) is a disincentive for health plan choice. These two prices are largely absent from the design of traditional health insurance plans, which feature relatively low deductibles and do not have health spending accounts. HSAs are likely to increase the range of health insurance options available to individuals. To date, however, very little is known about whether HSAs will be popular with individuals seeking private insurance coverage in general, and more specifically, with lowincome individuals who might have very limited access to alternative sources of health insurance. We will illustrate the usefulness of our results by applying them to a simulated national population in the United States to assess the potential of Health Savings Accounts (HSAs) for increasing the number of Americans, and especially those with low incomes, with health insurance. The research questions of this examination are: How does the introduction of Consumer Directed Health Plans (CDHPs) into mainstream health insurance affect plan choice? Specifically, what is the impact of the account, donut hole and deductible on the ownprice elasticity of CDHPs? What is the impact of the account, donut hole and deductible on the crossprice elasticity of insurance takeup among the uninsured? This paper builds upon the empirical findings from two earlier projects: a study of the impact of CDHPs on health plan choice supported by the Robert Wood Johnson Foundation (Parente, Feldman & Christianson, 2004); and an analysis of coverage and costs of alternative 2

4 policy proposals for reducing the uninsured through the use of HSA subsidies funded by the Assistant Secretary of the Department of Health and Human Services (Feldman et al, 2005; Parente et al, 2005). 2.0 Model Our model builds on the existing literature of highdeductible health plans (HDHPs) discussed by Keeler, Newhouse, and Phelps (1977), in which a high deductible introduces a kink in the consumer s budget constraint. In contrast, the health care account in a CDHP creates a double kink in the budget constraint as shown in Figure #1. This differentiates it from highdeductible health plans. The budget constraint of the CDHP plan starts out flat to reflect the monetary account a consumer or their employer arranges to purchase health care with a straight debit from the account for the price paid for each unit of medical care, such as a prescription drug or a physician office visit. The first kink in the budget constraint occurs when the account is exhausted and the consumer now faces the full price of medical care. The budget constraint continues to slope downward until the second kink at the point where the highdeductible threshold is reached. Depending on the highdeductible benefit design, the consumer faces either a limited coinsurance or nocoinsurance, as depicted in Figure #1. The design depicted is consistent with the early versions of the CDHP plan design (Christianson, Parente, Taylor, 2002). An additional feature of CDHP designs is the lack of a use it, or lose it provision. 1 Unspent dollars in the health account can be saved and used in following years to pay for future health contingencies, in addition to new health account dollars deposited. Figure 1 represents only a oneyear budget constraint and set of tangency points. At subsequent time periods, the 1 Such provisions are found in flexible spending accounts (FSAs), also known as Section 125 accounts for the section of the IRS code that authorizes them. Money in a flexible spending account is forfeited if not used by the end of the year. 3

5 budget constraint could be quite different depending upon how much money was saved. For example, a healthy individual with no expenditure from her account may save enough so that the budget constraint would become flat and she would face no kinks in that year. Thus, future models need to consider a multiperiod budget constraint as well as where the kinks could be located, based on contract availability, health account savings and variations in coinsurance as the market evolves. For this analysis, we restrict our attention to firstyear adopters of CDHPs so that the oneperiod conceptual model represented in Figure 1 applies. Figure 1 Conceptual Model of CDHP Money a b c d CDHP Budget A to B: Care Account B to C: Deductible C to D: Catastrophic insurance w/no coinsurance HRA/HSA deductible Medical Care Spending We propose that the CDHP design introduces two new prices for health insurance demand analyses to consider. First, the gap between the kinks in the budget constraint is a new form of outofpocket price that is neither deductible, copayment, nor premium. Also known as the donut hole, this amount represents a new price to which we would expect consumers to 4

6 have a negative demand response as the size of this potential outofpocket contribution increases. The second new price is the amount of the health savings account. This account can be funded by an individual, their employer, or some combination of the two. We would expect it to have a positive response in demand since it can be thought of as a form of consumer income. Typically, the unused portion of the account accumulates in the consumer s name over time and in the HSA design represents a genuine financial asset similar to a definedcontribution personal retirement account. By framing the insurance choice of a CDHP we can consider the donut hole and savings account as new prices in the demand for health insurance with expected negative and positive responses, respectively. This will enable us to estimate a model to examine the price sensitivity to different benefit options (i.e., premium, account, and donut hole) that could significantly affect takeup of CDHPs. To examine the choice of a CDHP, we recognize the need for a model than can accommodate existing mainstream health insurance designs, specifically preferred provider organizations (PPOs) and Health Maintenance Organizations (HMOs). We begin by considering a model where the utility of the j th plan to the i th consumer is specified as: (1) U ij = α j + β Z j + θ Z j X i + eij The utility function in (1) depends on plan benefit attributes (Z j ) including: Annual taxadjusted employee premium ($1000s dollars) Savings/reimbursement account size ($1000s dollars) Donut hole: difference between annual deductible and account size ($1000s dollars) Coinsurance rate (i.e.,.10 = 10% coinsurance) In addition, we allow interactions between plan and consumer attributes (X i ), such as age, sex, wage income, and family contract as well as planspecific constants (α j ). The random error term 5

7 e ij represents unobserved, consumerspecific aspects of utility from alternative j. This model provides an approach to estimate the price elasticity for the consumer s portion of their health insurance premium and coinsurance as well as the new CDHP arguments for the response from the savings account and donut hole. 3.0 Estimation Using the utility framework from (1), we assembled a set of data sources to complete an econometric analysis of plan choice. In this section, we first describe the data sources from employers and their contracted health plans. Second, we discuss our estimation approach using a conditional logistic regression to generate estimates of plan choice intercepts, health plan design attribute effects, and interaction effects. Third, ownprice elasticity responses are presented from the model. Finally, the conditional logistic regression coefficients are used in a microsimulation of plan choices, including an HSA choice, to develop a set of crossprice elasticities for the takeup of any health insurance. 3.1 Data Our primary source of data for this analysis was employee health plan choices from three large employers participating in a Robert Wood Johnson Foundation (RWJF) funded study of Consumer Directed Health Plans (CDHPs). In addition, we used the 2001 Medical Expenditure Panel Survey (MEPS) developed and supported by the Agency for Healthcare Research and Quality (AHRQ) and premium data for individual health insurance policies, including HSAs, from the ehealthinsurance.com web site. 2 The data from three large employers represented approximately 80,000 covered lives of information (including dependents). Two of the three employers were national firms with substantial populations of employees; one was a large employer located in Minnesota. Each of 6

8 these employers offers a CDHP along with traditional managed care plans. For the CDHP plans, each employer has received a takeup rate ranging between 4% and 15% in their first year offered. In our analysis we consider two models of CDHPs, the HSA or Health Savings Account and the HRA or Health Reimbursement Account arrangement. The HRA is usually offered by selfinsured employers with 1,000 or more covered lives. The health account in an HRA is not owned by the employee as an asset and is not transferable if the employee were to leave the firm. On the other hand, the HSA represents a benefit design where the health account is a financial asset owned by the individual. HSAs are being offered by employers as well, but their largest market segment is the individual and small group riskrated health insurance market. In our plan choice analysis, we principally focus on HRAs offered by three employers. Table 1 Plan Choice Descriptive Statistics Total Low HRA* High HRA HMO Low PPO Med PPO High PPO Observations 28, ,734 9,254 1,685 8,276 5,967 Employee Premium in $1, Health Account in $1, Donut Hole in $1, Coinsurance Female, 1=female, 0 else Age (scaled in 100 years) Income in $1, Family contract=1, else Highest value = * We use a Low HRA in our simulations for approximating an HSA benefit design. 7

9 Table 1 provides the descriptive statistics of the pooled employer sample. Across the three employers there were six possible health plan choices. A given employee faced a minimum of four choices and maximum of five choices. The total possible choices faced by an employee include: Lowoption HRA (low premium, larger deductible, some coinsurance) Highoption HRA (high premium, smaller deductible, no coinsurance) Lowoption PPO (low premium, smaller physician panel) Mediumoption PPO (median premium, coinsurance) Highoption PPO (high premium, less copayments than other PPOs, larger provider panel) HMO (e.g., group model, lower premium than PPOs) Amongst the six choices, the highoption PPO had the highest premium and the HMO had the lowest premium. The HRA premium was most similar to the medium PPO. The HRA had the highest deductible and the lowoption PPO had the greatest coinsurance. The highoption HRA had the least coinsurance. HMOs and medium PPOs had the greatest share of female enrollees and there was no statistically significant difference in the average age of employees among plans. Those who chose the highoption HRA had the highest income and those who chose the lowoption PPO had the lowest income. The HRA had the highest proportion of family contracts and the HMO had the least proportion of family contracts. 3.2 Plan Choice Estimation Using our pooled health plan choice data from the three employers, we specified a conditional logistic regression model similar to our earlier work (Parente, Feldman and Christianson, 2004) to estimate the utility function, based on the observed health plan choices. This method is motivated by a random utility function because there are errors in maximization due to imperfect perception and optimization, as well as errors due to unobserved relevant variables. We assume the random terms in equation (1), which represent unobserved, consumer 8

10 specific aspects of utility from alternative j, are independently and identically distributed with an extreme value distribution. Given this assumption, the probability that consumer i chooses alternative j from among the set of K possible plan choices can be represented as: (2) Pij = exp( α j + β Z j + θ Z j X i ) / exp( α k + β Z k + θ Z k X i ) K k = 1 A very important constraint in our modeling was that any plan attribute used in the model from the employer data also had to be available in the MEPS data to permit a prediction of uninsured takeup as part of our policy analysis. As a result, the key variables used in the plan choice model were: Aftertax premium paid by the employee 3 The amount of money in the employee s health reimbursement account (HRA), if any. The difference between the employee s plan deductible and the HRA. Coinsurance rate Employee s age Employee s gender (1=female, 0=male) Employee has a 2person or family contract=1, else =0 Employee s annual wage income. Also included in the model were alternativespecific constants (intercepts) for each of the possible health plan choices. These intercepts are used to capture planspecific features not represented by other identifiers of plan design. They are also included as interaction terms with age, gender, family status and income. The plan choice estimation results are presented as Table 2. The reference category for the regression was the highoption PPO. The signs of the coefficients for premium and coinsurance are negative as expected. The sign for the donut hole, representing the deductible 3 The price of each health plan (one of the Z j variables in the model) was measured by its tax adjusted outofpocket premium, because the three employers in our study let employees pay their outofpocket premiums with taxfree dollars (Dowd, et al., 2001). 9

11 less the amount in the health account, is negative as well. The sign for the health account variable is positive, showing that an increase in the starting account balance would lead to higher likelihood of a consumer selecting a CDHP. All four of these price responses are highly significant. With respect to the attributes of the consumer most likely to be associated with a particular health plan choice, we find that older workers are less likely to choose either a low or highoption HRA. This result is different than our descriptive statistics and suggests that once price and other demographic variables are accounted for, HRAs may be prone to more favorable selection compared with a highoption PPO. Similar to earlier findings by Parente et al (2004), higherincome employees are more likely to choose an HRA. In addition, no other health plan choice appears to be as positively linked to income as an HRA. Female employees are significantly less likely to prefer an HRA than a PPO. Also, employees selecting family coverage policies for benefits for spouses and dependents are less likely to choose a highoption PPO compared with an HRA. This result is also somewhat different than our bivariate descriptive results in Table 1 where the HRAs were associated with the greatest share of family contracts. 10

12 Table 2 Conditional Logistic Regression Adjusted Rsquare: ~.363 Standard Variable Coefficient Error TStatistic Pvalue Tax adjusted Employee Premium in $1, <.0001 Employee's Health Account in $1, <.0001 Between Deductible and Health Account in $1, <.0001 Coinsurance (e.g., 15% =.15) <.0001 PPO Medium Plan Intercept=1, else= PPO Low Plan Intercept=1, else= <.0001 High HRA Plan Intercept=1, else= <.0001 Low HRA* Plan Intercept=1, else= <.0001 HMO Plan Intercept=1, else= <.0001 Premium & Family Contract (0/1) Interaction <.0001 PPO Medium & Age (in 100 years) Interacttion <.0001 PPO Low & Age (in 100 years) Interaction High HRA & Age (in 100 years) Interaction <.0001 Low HRA & Age (in 100 years) Interaction <.0001 HMO & Age (in 100 years) Interaction <.0001 PPO Medium & Income (in $1,000) Interaction PPO Low & Income (in $1,000) Interaction High HRA & Income (in $1,000) Interaction <.0001 Low HRA & Income (in $1,000) Interaction <.0001 HMO & Income (in $1,000) Interaction PPO Medium & Female (0/1) Interaction PPOLow & & Female (0/1) Interaction High HRA & Female (0/1) Interaction Low HRA & Female (0/1) Interaction HMO & Female (0/1) Interaction <.0001 PPO Medium & Family Contract (0/1) Interaction PPO Low & Family Contract (0/1) Interaction <.0001 High HRA & Family Contract (0/1) Interaction Low HRA & Family Contract (0/1) Interaction HMO & Family Contract (0/1) Interaction <.0001 N=28,737 * We used Low HRA to create HSA 'predicting' coefficients for an individual version where the employee pays all (S) and the employer offered version where the premium and the account is heavily subsidized (E). 11

13 3.3 OwnPrice Elasticity Response In Table 3 we present the price elasticities for the four prices in our health plan choice model, including employee premium, health account, donut hole and coinsurance. These results are calculated based on an expected probability of a given health plan choice of 15%. 4 The largest of the elasticities is for the taxadjusted employee premium with a value of The least elastic response is for the employee health account. An interesting finding is the greater elasticity of coinsurance (.5405) compared with the donut hole (.2430), or the difference between the deductible and the health account. This result suggests that consumers have greater sensitivity to variations in coinsurance than to changes in the donut hole structure. This finding may challenge some detractors of CDHP plans who suggest consumers will not embrace a plan design with obvious increased cost sharing in the form of a large potential deductible, compared with coinsurance, a much more conventional method of costsharing. Table 3 PriceVariable Elasticity Tax adjusted Employee Premium in $1, Employee's Health Account in $1, Between Deductible and Health Account in $1, Coinsurance (e.g., 15% =.15) A health plan choice of 15% was chosen because two of the three employers had CDHP takeup rates that were close to that share. Also, 15% provides an approximately equal probability of plan choice among the six options analyzed in our conditional logistic regression. 5 The results reflected here do not include confidence intervals for the premium elasticity measures. However, given the large Tstatistics associated with the pricevariable coefficients, we doubt that any of the elasticity estimates would be insignificant. 12

14 These results can be very helpful to future insurance benefit designers. For example, if an employer wants to increase enrollment in a CDHP, it may get the best result by favoring an increase in deductibles over an increase in coinsurance, as well as increasing the size of the account, in order to hit the target enrollment with a projected premium constraint. 3.4 CrossPrice Elasticity Response on Takeup by the Uninsured Our plan choice estimates can be used for policy analysis. Specifically, we can use them to estimate what proposed tax credits to purchase HSAs could yield in terms of new insurance takeup by the uninsured. To examine this policy impact, we estimate crossprice elasticity responses with respect to subsidies applied to the HSA premium, health account, and the donut hole. To estimate the crossprice takeup elasticities, we used the plan choice regression results to predict plan choice probabilities for each MEPS sample respondent. 6 We developed two sets of plan choice predictions for the simulation: one set of data for workers with insurance offers and a second set for individuals who do not have employer offers of coverage. 7 This second set includes both uninsured individuals, as well as those who take up nongroup policies One group of individuals that we exclude from the simulation are nonoffered individuals who reported having employer group coverage through another household member. The analytic steps taken to develop this database are described in Parente et al (2005) and Feldman et al (2005). 6 The Medical Expenditure Panel Survey is an annual survey of the noninstitutionalized, civilian population in the U.S. For this analysis, we use the 2001 MEPS Household Component (HC), which is a publicuse file containing detailed demographic, health status, employment, insurance, medical care utilization and expenditure information on individuals. We restricted our attention to individuals who are 1964 years of age, not enrolled in public insurance programs, and not fulltime students. Our full sample has 16,282 individuals. When weighted to produce population estimates, this corresponds to 147,955,033 nonelderly adults in the United States. 7 We converted HMO copays to actuariallyequivalent coinsurance rates for predicting the HMO enrollment probability. 13

15 In order to predict plan choice probabilities, we needed to develop some specific assumptions about benefit plan design and premiums for individual plans. To get premium estimates, we used MEPS linked insurance data to develop a hedonic price model to predict premiums for individual plans. Once we estimated the premiums we inflated them from 2001 prices to 2005 prices based on medical insurance price inflation during the period. In addition, we also taxadjust the outofpocket premiums used in the national simulation. 8 One significant issue with our simulation is that we were not able to predict whether an individual would take up insurance in the employeroffered market or purchase insurance in the individual market. We faced this limitation because the CDHP employer data only include information on offered workers who held coverage. To address this issue, we calibrated our model to accurately reflect both the actual percentage of people who turn down employer offers and the actual percentage of people in the individual market who are uninsured. Our crossprice elasticity estimates were based on a series of steps designed to predict a nationally representative sample of health plan choices. We first estimated the probability of being uninsured assuming the presence of HRAs in the group market and HSAs in the individual market. This first involved generating predicted estimates of plan choice takeup using the MEPS data and our conditional logistic regression coefficients. Since there were no uninsured in our employer databases, we created intercept variables for the uninsured and turndown populations. For our simulations, we also assumed the highoption HRA represents the overall HRA market and the lowoption HRA represents the HSA market. Our rationale for assigning the HSA to the lowoption HRA is that the actuarial benefit of lowoption HRA is more compatible as an HSA. These intercepts allowed us to calibrate the predictions to represent 8 We are only able to adjust at the federal income tax level since the MEPS does not provide statespecific locations of the survey respondents. 14

16 national estimates of the uninsured. We then added subsidies to premiums, health accounts, and deductibles in separate simulations. Since we are generating personspecific probabilities of plan choice, we are also able to tally by different subgroups of the US population including by income quartile and family insurance contract type. From the status quo and other simulations we used the expression (3) to generate crossprice elasticities. (3) crossprice elasticity = (pr uninsured status quo pr uninsured HSA premium) * (HSA quo/pr uninsured status quo) Table 4 presents crossprice elasticity estimates with respect to a $1,000 increase in the employee s outofpocket premium. 9 At lower incomes we find far less elastic response to an increase in premium than at higher income. This response is consistent in both single and family insurance contracts. Subsidy responsiveness is higher for family contracts than single contracts. This finding suggests plan choice decisionmakers for family contracts have greater interest in taking up insurance than purchasers of single insurance contracts because more lives are at risk of facing random catastrophic illness or injury. The overall response is similar to other work by Marquis and Long (1995) and Marquis et al. (2004), in the individual market as well as Chernew et al., 1997; Shiels et al., 1999; Blumberg et al., 2001; and Gruber and Washington, One interesting aspect of our findings is the differences in elasticity with respect to income. Initially, we expected that individuals with lower incomes would respond more positively to a subsidy than those with high incomes. The result can be explained by our earlier observation that the HRA plan choices were positively associated with income. Given the construction of crossprice elasticity estimates and the preference of higherincome individuals for CDHPs, it would be expected that CDHP designs would have a lesselastic price response 9 The $1,000 increase is an addition to the taxadjusted employee outofpocket premium. 15

17 among lowerincome individuals. The opposite effect would be found if we were to subsidize the premiums for HMO plans since wealthier individuals have less desire for an HMO compared with persons with less income. Table 4 Cross priceelasticity* of insurance takeup with respect to HSA premium subsidy Single Adults with Income Quartile Adults Dependents 0 to to to to All *Calculated as the MEPS surveyweighted average of each person s: (pr uninsured status quo pr uninsured HSA premium) * (HSA quo/pr uninsured status quo) Table 5 presents the crossprice elasticities of insurance takeup with respect to a decrease in the donut hole as well as an increase in account size. We find a similar result to Table 4 with respect to income where takeup response is greater among the wealthier citizens. Consistent with our ownprice elasticity response for the donut hole and the account, we find greater crossprice elasticity for a decrease in the donut hole than an increase in the account size. Interestingly, a reduction in the donut hole will yield roughly the same magnitude of takeup responses as a reduction in the premium paid from a subsidy. 16

18 Table 5 Cross priceelasticities of uninsured takeup with respect to donut hole and account with respect to a with respect to a Income Quartile decrease in donut increase in account size 0 to to to to All Implications This analysis offers new insights on price effects from the introduction of CDHPs into mainstream health insurance choices. Specifically, we find a positive impact of the account and negative responses to the donut hole. The donut hole has a far more elastic price response than the health account. In terms of new policy options to reduce the uninsured through tax credits targeted at HSAs, we find, on average, elastic crossprice responses to increase the takeup of insurance among those without coverage. However, we also find that takeup is considerably greater among the higherincome population due to wealthier individuals having a distinct preference for CDHPs among all other health plan choices. We discuss the policy implications of this research as well as the next steps for continued investigation as the CDHP market continues to evolve. 17

19 4.1 Policy Implications There are three policy implications from this analysis. The first is that plan design matters if the goal of targeting subsidies or other economic incentives, through the use of a tax exemption, is to reduce the uninsured by an increase in takeup in the individual market. For example, we find greater takeup from a reduction in the donut hole than an increase in the account size. A second implication is that HSA subsidies might not be the most effective economic incentive to increase takeup among the lowerincome uninsured population. This population is likely to face a greater financial burden to purchase health insurance than wealthier individuals. One way to design better incentives is adjust the size of the subsidy based on income. In fact, the 2004 Republican presidential campaign proposed such a meanstested policy prescription targeting HSAs as the plan choice to increase takeup among the uninsured. The approach used in this paper can be extended and applied to approximate an optimal sliding scale for means testing. It can also be used to compare the relative efficiency of HSAs as the target of tax credits to increase takeup as opposed to HMO or PPO benefit designs. A third implication is the relative complexity of the use of economic incentives coupled with HSAs to increase takeup. Critical questions as to when the tax credit or subsidy would be available would need to be addressed. In this analysis, we assume that the consumer will realize an immediate benefit from the tax credit. However, the tax credit may take 12 to 16 months to materialize if it is made available as an afterpurchase credit. If the credit is available at time of purchase, how will eligibility be verified? At the very least, policy makers should try to avoid 18

20 the unnecessary administrative eligibility barriers like those experienced in the Trade Adjustment Assistance Reform Act of One advantage of using the HSAs is that the supply of the plans is far more abundant across the nation. Almost all major commercial insurers, including staffmodel HMOs such as Kaiser Permanente, offer one or more variant of the plans. A positive macroeconomic externality of favorable selection into HSAs by the young and the healthy would yield an increase in personal savings to stem the rate of erosion of national savings. Furthermore, most plan designs have also included nocost preventive care for physicals and wellchild visits and some even offer free prescription drugs for chronic illnesses such as asthma or diabetes that require regular pharmaceutical use for preventive care. 4.2 Research Implications This research provides an economic model and an empirical test for CDHP benefit and plan choice. Specifically, we respond to the early call from Rosenthal (2004) for a starting template to model and evaluate donut hole economics. Although Rosenthal s intent was to highlight how the use of donut hole is simply reinvented costsharing to give consumers a greater incentive to engage in health care investment as well as temporary hedge against health care premium increases rising faster than wage rate increases, it does introduce a second kink in the traditional highdeductible budget constraint. Our analysis provides an approach to model CDHPs in future plan choice analyses found commonly in the health economics literature. Additional complexities will need to modeled, such as the role of free preventive services running concurrently with other services on the budget constraint. To date, we have found the expenditure associated with these services to be fairly minor. However, the recent inclusion of 10 Lack of knowledge by employers and employees were the principal barriers prohibiting greater success of the Trade Adjustment Assistance Reform Act of Future initiatives targeted at specific populations may require aggressive public education campaigns. 19

21 prescription drugs could lead to greater impact on the overall budget constraint that would require further consideration. Another further development will be the changes in the starting point of the account due to rollover of unused resources. We anticipate that the rollover contribution may very well be another price that needs to be considered or, at the very least will show the effect of rollover dollars in the difference between the deductible and the account plus rollover. 11 A future research topic will be the welfare implications of CDHP plans. The use of HSAs opens the possibility of a consumer changing her allocations between long and short term savings. A consumer may decide to contribute more to her HSA if she decides her health needs are more critical than retirement savings and she finds herself healthier, but less wealthy, in old age. Alternatively, a consumer may decide to invest more in retirement and underinvest in her health only to find that she has not been able to live a long healthy life to enjoy retirement due to a premature death. These questions can be addressed using a combination of health plan choice and retirement savings information available from employers. Pursuing this question will help address the open policy question of whether the young and healthier might invest more in health and, by default, increase their retirement savings. This work would contribute to the joint health and retirement decision literature summarized by Gruber & Madrian (1995). 5.0 Conclusions Using an adaptation of the highdeductible health plan economic model, we developed a consumer utility model of health plan choice that considers the new developments in the CDHP market. We found a positive impact of the account and a negative response to the donut hole. With respect to sensitivity of the price effects, the donut hole has a far more elastic response than 11 In our empirical analysis, rollover was not an issue since we used the first year of CDHP offer for all three employers. 20

22 the health account. In terms of new policy options to reduce the uninsured through tax credits targeted at HSAs, we find, on average, elastic crossprice responses to increase the takeup of insurance among those without coverage. However, we also find that takeup is considerably greater among the higherincome population due to wealthier individuals having a distinct preference for CDHPs among all other health plan choices. 21

23 References Christianson J, Parente ST, Taylor R. Defined contribution health insurance products: development and prospects. Health Affairs 2002; 21(1):4964. Dowd B, Feldman R, Maciejewski M,. Pauly MV. The effect of taxexempt outofpocket premiums on health plan choice. National Tax Journal 2001; 44(4): Feldman, R. Parente, S., Abraham, J., Christianson, J. Assessing the Impact of Health Savings Accounts on Insurance and Coverage Costs. Forthcoming in Health Affairs in November, Gruber, J., and B. Madrian. 1995; Health Insurance Availability and the Retirement Decision: American Economic Review. 85: Keeler, Emmett B & Newhouse, Joseph P & Phelps, C E, "Deductibles and the Demand for Medical Care Services: The Theory of a Consumer Facing a Variable Price Schedule under Uncertainty," Econometrica, vol. 45(3), pages Parente, S.T., Feldman, R., Abraham, J., Christianson, J. Final Technical Report: Assessing the Impact of Health Savings Accounts on Insurance and Coverage Costs. Department of Health and Human Services, Washington, DC, July, Parente ST, Feldman R, and Christianson JB. Employee Choice of Consumer Driven Health Insurance in a Multiplan, Multiproduct Setting, Health Services Research Vol. 39, No. 4, Part II (August 2004), pp Rosenthal, M. DoughnutHole Economics, Health Affairs.2004; 23: U.S. Congress (108 th, 1 st, 2003). Medicare Prescription Drug, Improvement, and Modernization Act of 2003 conference report to accompany H.R.1. (Washington, DC, U.S. G.P.O., 2003). U.S. Department of the Treasury. General Explanations of the Administration s Fiscal Year 2005 Revenue Proposals. February,

24 Appendix A National Bureau of Economic Research Summer Institute Working Paper The following working paper describes the full policy simulations mentioned to in the preceding text and are provided as additional background material. 23

25 Assessing the Impact of Health Savings Accounts on Insurance and Coverage Costs Stephen T Parente, Ph.D. Roger Feldman, Ph.D. Jean Abraham, Ph.D. Jon B Christianson, Ph.D. University of Minnesota JEL Codes: I1, D12, C93 July, 2005 Do not quote or cite without permission of authors. Paper presented at the Health Care Program of the 2005 National Bureau of Economic Research Summer Institute, Cambridge, MA, USA on July 29, Corresponding Author: Stephen T. Parente, Ph.D., Assistant Professor, Department of Finance, Carlson School of Management, University of Minnesota, th Ave., Rm. 3149, Minneapolis, MN 55455, , sparente@csom.umn.edu, This paper was funded from joint support from the Robert Wood Johnson Foundation's initiative on Changes in Health Care Financing and Organization and DHHS Contract HHSP P: Analytic Support in Assessing the Impact of Health Savings Accounts on Insurance Coverage and Costs. 1

26 Introduction Consumer directed health plans (CDHPs) are attracting attention from consumers, employers, and policymakers. CDHPs are highdeductible health insurance plans coupled with a taxadvantaged account, which can be used to pay for eligible medical expenses. If an enrollee spends all of the dollars in the health spending account during a given year, she then spends her own money until the health insurance plan deductible requirement is met. The 2003 Medicare Modernization Act (MMA) gave a huge boost to CDHPs by approving taxadvantaged health savings accounts (HSAs) for certain highdeductible health insurance plans. Section 1201 (and subsequent guidance by the Treasury Department) approved a new form of health plan known as a Health Savings Account or HSA. Beginning on January 1, 2004, most nonelderly individuals can purchase a health plan with an annual deductible of at least $1,000 for an individual and $2,000 for a family, coupled with a taxadvantaged account to which both the employer and the enrollee may contribute. Total annual contributions can be as large as the plan s deductible amount (up to $5,000 for an individual and $10,000 for a family). Contributions to the HSA, as well as interest and investment earnings in the HSA, are not taxable, and unlike previous designs, the HSA is fully portable so an individual may use it without being dependent on the provisions of a particular employer. In response to MMA, mainstream insurers such as Blue Cross and Blue Shield plans and UnitedHealth Group have hurried to offer HSAs. Research Questions The purpose of this project is to examine the potential impact of Health Savings Accounts (HSAs) on increasing the number of Americans, and especially those with low incomes, with health insurance. The research questions of this examination are: What is the expected takeup rate of HSAs in the individual market from the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 (MMA)? What is the impact of the Administration s proposed HSA subsidies? o Takeup rate of HSAs o Impact on the uninsured o Cost of the subsidy What is the impact of other possible subsidy designs? Background Congress recently enacted and the President signed into law the Medicare Prescription Drug, Improvement, and Modernization Act of 2003 (MMA) 1. The MMA contains a number of significant changes to the Medicare program, including the introduction of a prescription drug 1 P.L

27 benefit and the use of competitive bidding to determine which plans (and at what prices) will be allowed to participate in the Medicare program. The MMA also establishes Health Savings Accounts (HSAs), which are taxadvantaged savings vehicles that can be used to pay for medical expenses incurred by individuals and their dependents. 2 Prior to MMA, employers were able to provide their employees either Flexible Spending Accounts (FSAs) or Health Reimbursement Accounts (HRAs), which paid for qualified medical expenses out of pretax wage income. In addition, employees in small firms, the selfemployed, and others purchasing insurance in the nongroup market could establish a taxadvantaged Medical Savings Account (MSA), which could be used in connection with a highdeductible insurance plan. In general, HSAs are similar to MSAs in that both accounts are taxadvantaged and must be used in combination with a highdeductible insurance policy. HSAs, however, are more expansive than MSAs, in that eligibility is not limited to employees of small firms and the selfemployed. Larger employers can now offer an HSA, which was not the case previously with MSAs. HSAs are likely to increase the range of health insurance options available to individuals. To date, however, very little is known about whether HSAs will be popular with individuals seeking private insurance coverage in general, and more specifically, with lowincome individuals who might have very limited access to alternative sources of health insurance. This project builds upon an existing effort supported by The Robert Wood Johnson Foundation. The objective of this analysis is to produce estimates of coverage and costs of alternative policy proposals for reducing the uninsured through the use of HSA subsidies. This is being accomplished by: (1) developing an analytic database that uses information from the 2001 Medical Expenditure Panel Survey (MEPS) as well as the Contractor s existing employerbased data files; (2) estimating a health plan choice model; and (3) using these results to perform a microsimulation of HSA takeup. Data & Analytic Approach Three data sources were used to complete this analysis. These data sources and the steps taken to prepare the database are described in Figure 1. The data sources include: 1. The 2001 Medical Expenditure Panel Survey (MEPS) developed and supported by the Agency for Healthcare Research and Quality (AHRQ). 2. Health plan choice data from three large employers participating in a Robert Wood Johnson Foundation (RWJF)funded study on Consumer Directed Health Plans (CDHPs). 3. Premium data for individual health insurance policies from the ehealthinsurance.com web site. 2 See Section 1201 of the MMA. 3

28 These data sources were used for three major analysis tasks as outlined in Figure 1: Model estimation; Choice Set Assignment/Prediction; and Policy Simulation. As illustrated in this figure, often more than one database was required to complete the task. Integral to this analysis was the use of consumer directed health plan data from three large employers working with the study investigators. Below, we provide greater detail on database attributes, use of the databases, and the analytic methods used. Analysis Design Figure 1 Data Sources MEPS CDHPs ehealthinsurance Model Estimation Choice set Assignment/ Prediction Estimate plan offerings using linked data Estimate hedonic premium regression Assign plan choices to full MEPS sample Use parameter estimates to predict plan choice probabilities for MEPS Merge employer data Estimate plan choice regression Define HSA plan design & premium Policy Simulation Rescale takeup rates Simulate impact of proposed policies Database Descriptions Medical Expenditure Panel Survey (2001): The Medical Expenditure Panel Survey is an annual survey of the noninstitutionalized, civilian population in the U.S. For this project, we use the 2001 MEPS Household Component (HC), which is a publicuse file containing detailed demographic, health status, employment, insurance, medical care utilization and expenditure information on individuals. We restrict our attention to individuals who are 1964 years of age, not enrolled in public insurance programs, and not fulltime students. Our full sample has 16,282 individuals. When weighted to produce population estimates, this corresponds to 147,955,033 nonelderly adults in the United States. A breakdown of the 1964 population for 2001 is provided in Figure 2. 4

29 Figure 2 Consumer Directed Health Plan data ( ): The project investigators had access to deidentified data on the selection of health plans by employees, as well as their demographics. For this analysis, data from three large employers representing approximately 80,000 covered lives of information (including dependents) were available. Two of the three employers were national firms with substantial populations of employees; one was a large employer located in Minnesota. Each of these employers has offered a CDHP along with other traditional managed care plnas. For the CDHP plans, each employer has received a takeup rate ranging between 4% and 15% in their first year offered. ehealthinsurance Data (2005 HSA premiums): We used the ehealthinsurance.com web site to sample premiums for Health Savings Accounts. The web site provides a monthly estimated premium cost based on county of location, age, family size, and health history. 3 For individual contracts, we assumed a single, nonsmoking male age 40. For family contracts, we assumed the policyholder was a nonsmoking, 40 year old, married man with a spouse and two children under the age of 10. As a robustness check, we randomly sampled fifty other large metropolitan statistical areas around the U.S., and found that only two did not offer an HSA policy and moreover, that most policies had comparable premiums. Model Estimation The model estimation had several steps. As a first step, we pooled the data from the three employers offering CDHPs to estimate a conditional logistic plan choice model similar to our 3 For our first round of simulations we used Santa Clara County, California, as the geographic location. For our reported results we use premiums based on a sample taken from 100 metro areas. 5